plotlyflexdashboardplotlySo maybe you’ve just got the hang of visualizing data with
ggplot2. You’ve started creating some pretty awesome graphs
and perhaps even started to get some preferences regarding background or
colors. Moreover, because of the wide use and support, you can look up
almost anything on the internet, and new features are being developed
all the time. With all these benefits, why start learning another data
visualization package at all?
In all due honesty, the packages are very similar in terms of speed,
user friendliness and customization tools, but plotly has
one advantage over ggplot. plotly can create
interactive graphs. This makes the package great for website
development, even if you’re just creating a simple dashboard. If you’re
working in a team with others, plotly can also be handy
because it’s simpler integrate with other programming languages such as
Javascript and Python.
Let’s compare the use of ggplot2 and plotly
on making our very simple histogram from the gapminder dataset. As you
can see, the syntax is slightly different, but the main components
remain. In both syntaxes, you have to specify (1) what your dataset is,
(2) which variable(s) you are plotting and (3) what kind of plot you are
making.
library(ggplot2)
library(plotly)
gapminder <- gapminder::gapminder
gapminder %>%
ggplot(aes(lifeExp)) +
geom_histogram()
gapminder %>%
plot_ly(x = ~lifeExp,
type = "histogram")